Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs

TL;DR

A robust fall detection system that does not require any additional sensors or high-powered hardware, and uses pose estimation techniques, combined with threshold-based analysis and a voting mechanism to effectively distinguish between fall and non-fall activities.

Abstract

Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of life. Current fall detection solutions typically rely on sensor-based systems that require dedicated hardware, or on video-based models that demand high computational resources and GPUs for real-time processing. In contrast, this paper presents a robust fall detection system that does not require any additional sensors or high-powered hardware. The system uses pose estimation techniques, combined with threshold-based analysis and a voting mechanism, to effectively distinguish between fall and non-fall activities. For pose detection, we leverage MediaPipe, a lightweight and efficient framework that enables real-time processing on standard CPUs with minimal computational overhead. By analyzing motion, body position, and key pose points, the system processes pose features with a 20-frame buffer, minimizing false positives and maintaining high accuracy even in real-world settings. This unobtrusive, resource-efficient approach provides a practical solution for enhancing resident safety in old age homes, without the need for expensive sensors or high-end computational resources.

Figures (3)

• Figure 1: Flowchart of the Fall Detection System.
• Figure 2: Pose Detection
• Figure 3: Fall Detected